Data Handling and Statistics 3 is the third applied statistics units offered by the School of Natural Sciences (Mathematics). It provides an extension of the concepts, methods and tools introduced in KMA253. It is a 'hands-on' course in which the emphasis is on the development of skills in the selection and application of upper-level statistical methodology. Emphasis is also placed on the presentation of statistical analyses in a written format that promotes reproducible research. Topics covered in the course include: hypothesis testing, experimental design, inference, analysis presentation, generalised linear modelling; mixed-effects modelling, multinomial regression, and model selection. Expertise with the statistical computing language R and RStudio will be extended, including the application of R Markdown for promoting reproducible research. Examples will be drawn from the biological, physical and social sciences.
|Unit name||Data Handling and Statistics 3|
|Faculty/School||College of Sciences and Engineering
School of Natural Sciences
|Available as student elective?||Yes|
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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
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* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).
You cannot enrol in this unit as well as the following:
3 x 1hr face-to-face lectures, 1x1hr tutorial, 1x1-hr computer lab sessions.
3 class tests (46%), projects (54%)
|Timetable||View the lecture timetable | View the full unit timetable|
Fränzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jérôme Guélat, Bettina Almasi, and Pius Korner-Nievergelt (2015) Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. Academic Press.
Claus Thorn Ekstrom (2017) The R primer. Second edition. CRC Press.
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.